Wholesale, Retail, Slice and Dice
Data warehouses and data marts are very similar technologies, say experts, but they usually service different types of clients. For instance, a warehouse typically contains a massive amount of data from across an enterprise, says John Kopcke, chief technology officer at Hyperion Solutions Corp., a maker of analytical software in Sunnyvale, Calif.
Data marts tend to be smaller and dedicated to a single division or line of business. Data warehouses are "similar to a real food warehouse, storing massive amounts of food and then distributing subsets of food to grocery stores [the marts] for people to access [or] purchase," says Kopcke.
A data mart can run in size from megabytes to gigabytes, says Tho Nguyen, director of data warehousing strategy at SAS Institute Inc. in Cary, N.C., whereas data warehouses usually run from gigabytes to terabytes.
Consider a data mart that supports a firm's cellophane-tape division. It might contain relevant facts about making cellophane tape - suppliers, deliveries, rates, quality control information - says Schiff.
However, the uncontrolled proliferation of such data marts can become an IT nightmare unless each data mart uses standard naming and cataloging schemes and compatible data types. The last thing you want are data marts that can't talk to one another.
Users tend to assemble a warehouse from different pieces of technology, then customize it to meet their needs, rather than just put it together out of the box. Schiff notes that warehouses are often built using relational databases, because the relational model can more efficiently store and organize the huge amounts of information that make up a high-volume, multipurpose data warehouse. However, getting data from many large relational tables can require massive amounts of processing and storage.
For that kind of slice-and-dice analysis, data marts use multidimensional databases geared for quick responses with multiple elements. Often-selected data from a data mart is fed into a smaller database called a data cube for intensive processing.
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Taming Data Chaos
Stories in this report:
- Taming Data Chaos
- The Story So Far
- Merging Data Silos
- Beware of Data Overload from External Data
- Learn to Manage Data, Not Crises
- Data's Tower of Babel
- Extracting Dollars From Data
- Why ROI is so Elusive
- Collections of Data: Bases, Marts, Warehouses
- The Power of Location
- Seeding for Data Growth
- The Search is On
- The Data Designers
- Demise of the Disk Era
- Dawn of a New Database
- Keeping CFOs Happy
- Case Studies in Data Management
- Hot Issues: Scalability and Data Integration